






"""simple docstring"""







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor, CLIPProcessor





@require_vision
class 	UpperCamelCase_			(							unittest.TestCase	):



  def 		UpperCamelCase_					(   self					)      ->      List[str]:



         """simple docstring"""





         UpperCAmelCase						     =      tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase						     =      ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
         # fmt: on
         UpperCAmelCase						     =      dict(zip(snake_case__			,		range(len(snake_case__					)					)					)					)
         UpperCAmelCase						     =      ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
         UpperCAmelCase						     =      {"""unk_token""": """<unk>"""}

         UpperCAmelCase						     =      os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES["""vocab_file"""]					)
         UpperCAmelCase						     =      os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES["""merges_file"""]					)
         with open(self.vocab_file			,		"""w"""			,		encoding="""utf-8"""					) as fp:
                fp.write(json.dumps(snake_case__					) + """\n"""					)
         with open(self.merges_file			,		"""w"""			,		encoding="""utf-8"""					) as fp:
                fp.write("""\n""".join(snake_case__					)					)

         UpperCAmelCase						     =      {
             """do_resize""": True,
             """size""": 20,
             """do_center_crop""": True,
             """crop_size""": 18,
             """do_normalize""": True,
             """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
             """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
         }
         UpperCAmelCase						     =      os.path.join(self.tmpdirname			,		snake_case__					)
         with open(self.image_processor_file			,		"""w"""			,		encoding="""utf-8"""					) as fp:
                json.dump(snake_case__			,		snake_case__					)



  def 		UpperCamelCase_					(   self			,		**snake_case__					)      ->      Any:



         """simple docstring"""





         return CLIPTokenizer.from_pretrained(self.tmpdirname			,		**snake_case__					)



  def 		UpperCamelCase_					(   self			,		**snake_case__					)      ->      int:



         """simple docstring"""





         return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,		**snake_case__					)



  def 		UpperCamelCase_					(   self			,		**snake_case__					)      ->      List[str]:



         """simple docstring"""





         return CLIPImageProcessor.from_pretrained(self.tmpdirname			,		**snake_case__					)



  def 		UpperCamelCase_					(   self					)      ->      Optional[int]:



         """simple docstring"""





         shutil.rmtree(self.tmpdirname					)



  def 		UpperCamelCase_					(   self					)      ->      List[Any]:



         """simple docstring"""





         UpperCAmelCase						     =      [np.random.randint(2_55			,		size=(3, 30, 4_00)			,		dtype=np.uinta					)]

         UpperCAmelCase						     =      [Image.fromarray(np.moveaxis(snake_case__			,		0			,		-1					)					) for x in image_inputs]

         return image_inputs



  def 		UpperCamelCase_					(   self					)      ->      Optional[int]:



         """simple docstring"""





         UpperCAmelCase						     =      self.get_tokenizer()
         UpperCAmelCase						     =      self.get_rust_tokenizer()
         UpperCAmelCase						     =      self.get_image_processor()

         UpperCAmelCase						     =      CLIPProcessor(tokenizer=snake_case__			,		image_processor=snake_case__					)
         processor_slow.save_pretrained(self.tmpdirname					)
         UpperCAmelCase						     =      CLIPProcessor.from_pretrained(self.tmpdirname			,		use_fast=snake_case__					)

         UpperCAmelCase						     =      CLIPProcessor(tokenizer=snake_case__			,		image_processor=snake_case__					)
         processor_fast.save_pretrained(self.tmpdirname					)
         UpperCAmelCase						     =      CLIPProcessor.from_pretrained(self.tmpdirname					)

         self.assertEqual(processor_slow.tokenizer.get_vocab()			,		tokenizer_slow.get_vocab()					)
         self.assertEqual(processor_fast.tokenizer.get_vocab()			,		tokenizer_fast.get_vocab()					)
         self.assertEqual(tokenizer_slow.get_vocab()			,		tokenizer_fast.get_vocab()					)
         self.assertIsInstance(processor_slow.tokenizer			,		snake_case__					)
         self.assertIsInstance(processor_fast.tokenizer			,		snake_case__					)

         self.assertEqual(processor_slow.image_processor.to_json_string()			,		image_processor.to_json_string()					)
         self.assertEqual(processor_fast.image_processor.to_json_string()			,		image_processor.to_json_string()					)
         self.assertIsInstance(processor_slow.image_processor			,		snake_case__					)
         self.assertIsInstance(processor_fast.image_processor			,		snake_case__					)



  def 		UpperCamelCase_					(   self					)      ->      str:



         """simple docstring"""





         UpperCAmelCase						     =      CLIPProcessor(tokenizer=self.get_tokenizer()			,		image_processor=self.get_image_processor()					)
         processor.save_pretrained(self.tmpdirname					)

         UpperCAmelCase						     =      self.get_tokenizer(bos_token="""(BOS)"""			,		eos_token="""(EOS)"""					)
         UpperCAmelCase						     =      self.get_image_processor(do_normalize=snake_case__			,		padding_value=1.0					)

         UpperCAmelCase						     =      CLIPProcessor.from_pretrained(
             self.tmpdirname			,		bos_token="""(BOS)"""			,		eos_token="""(EOS)"""			,		do_normalize=snake_case__			,		padding_value=1.0					)

         self.assertEqual(processor.tokenizer.get_vocab()			,		tokenizer_add_kwargs.get_vocab()					)
         self.assertIsInstance(processor.tokenizer			,		snake_case__					)

         self.assertEqual(processor.image_processor.to_json_string()			,		image_processor_add_kwargs.to_json_string()					)
         self.assertIsInstance(processor.image_processor			,		snake_case__					)



  def 		UpperCamelCase_					(   self					)      ->      Optional[Any]:



         """simple docstring"""





         UpperCAmelCase						     =      self.get_image_processor()
         UpperCAmelCase						     =      self.get_tokenizer()

         UpperCAmelCase						     =      CLIPProcessor(tokenizer=snake_case__			,		image_processor=snake_case__					)

         UpperCAmelCase						     =      self.prepare_image_inputs()

         UpperCAmelCase						     =      image_processor(snake_case__			,		return_tensors="""np"""					)
         UpperCAmelCase						     =      processor(images=snake_case__			,		return_tensors="""np"""					)

         for key in input_image_proc.keys():
                self.assertAlmostEqual(input_image_proc[key].sum()			,		input_processor[key].sum()			,		delta=1e-2					)



  def 		UpperCamelCase_					(   self					)      ->      Any:



         """simple docstring"""





         UpperCAmelCase						     =      self.get_image_processor()
         UpperCAmelCase						     =      self.get_tokenizer()

         UpperCAmelCase						     =      CLIPProcessor(tokenizer=snake_case__			,		image_processor=snake_case__					)

         UpperCAmelCase						     =      """lower newer"""

         UpperCAmelCase						     =      processor(text=snake_case__					)

         UpperCAmelCase						     =      tokenizer(snake_case__					)

         for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]			,		encoded_processor[key]					)



  def 		UpperCamelCase_					(   self					)      ->      List[str]:



         """simple docstring"""





         UpperCAmelCase						     =      self.get_image_processor()
         UpperCAmelCase						     =      self.get_tokenizer()

         UpperCAmelCase						     =      CLIPProcessor(tokenizer=snake_case__			,		image_processor=snake_case__					)

         UpperCAmelCase						     =      """lower newer"""
         UpperCAmelCase						     =      self.prepare_image_inputs()

         UpperCAmelCase						     =      processor(text=snake_case__			,		images=snake_case__					)

         self.assertListEqual(list(inputs.keys()					)			,		["""input_ids""", """attention_mask""", """pixel_values"""]					)

         # test if it raises when no input is passed
         with pytest.raises(snake_case__					):
                processor()



  def 		UpperCamelCase_					(   self					)      ->      List[str]:



         """simple docstring"""





         UpperCAmelCase						     =      self.get_image_processor()
         UpperCAmelCase						     =      self.get_tokenizer()

         UpperCAmelCase						     =      CLIPProcessor(tokenizer=snake_case__			,		image_processor=snake_case__					)

         UpperCAmelCase						     =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase						     =      processor.batch_decode(snake_case__					)
         UpperCAmelCase						     =      tokenizer.batch_decode(snake_case__					)

         self.assertListEqual(snake_case__			,		snake_case__					)



  def 		UpperCamelCase_					(   self					)      ->      int:



         """simple docstring"""





         UpperCAmelCase						     =      self.get_image_processor()
         UpperCAmelCase						     =      self.get_tokenizer()

         UpperCAmelCase						     =      CLIPProcessor(tokenizer=snake_case__			,		image_processor=snake_case__					)

         UpperCAmelCase						     =      """lower newer"""
         UpperCAmelCase						     =      self.prepare_image_inputs()

         UpperCAmelCase						     =      processor(text=snake_case__			,		images=snake_case__					)

         self.assertListEqual(list(inputs.keys()					)			,		processor.model_input_names					)






'''simple docstring'''


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, TensorType

UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """imagegpt"""
   _a    						=   ["""past_key_values"""]
   _a    						=   {
       """hidden_size""": """n_embd""",
       """max_position_embeddings""": """n_positions""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }







   def __init__(						self   ,				UpperCamelCase=512 + 1   ,				UpperCamelCase=32 * 32   ,				UpperCamelCase=512   ,				UpperCamelCase=24   ,				UpperCamelCase=8   ,				UpperCamelCase=None   ,				UpperCamelCase="quick_gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-5   ,				UpperCamelCase=0.02   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				**UpperCamelCase   ,				)					->     int:
     __a								=       vocab_size
     __a								=       n_positions
     __a								=       n_embd
     __a								=       n_layer
     __a								=       n_head
     __a								=       n_inner
     __a								=       activation_function
     __a								=       resid_pdrop
     __a								=       embd_pdrop
     __a								=       attn_pdrop
     __a								=       layer_norm_epsilon
     __a								=       initializer_range
     __a								=       scale_attn_weights
     __a								=       use_cache
     __a								=       scale_attn_by_inverse_layer_idx
     __a								=       reorder_and_upcast_attn
     __a								=       tie_word_embeddings

     super().__init__(tie_word_embeddings=UpperCamelCase   ,				**UpperCamelCase   )





class  __lowercase    (	__magic_name__			):







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('input_ids', {0: 'batch', 1: 'sequence'}),
         ]   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = 1   ,				UpperCamelCase = -1   ,				UpperCamelCase = False   ,				UpperCamelCase = None   ,				UpperCamelCase = 3   ,				UpperCamelCase = 32   ,				UpperCamelCase = 32   ,				)					->     Mapping[str, Any]:
     __a								=       self._generate_dummy_images(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
     __a								=       dict(preprocessor(images=UpperCamelCase   ,				return_tensors=UpperCamelCase   )   )

     return inputs



